View of Music Used for Parkinson’s Dancing Classes: Rationale and Validity or Instruments and Methods


Music Used in Dancing Classes for People with Parkinson’s Disease: Rationale and Validity of Instruments and Methods

By Winifred A. Beevers, Meg E Morris, & Janet McConville


We conducted a systematic search of available literature on methods of analysis for the music used in exercise therapy interventions. The population focus was Parkinson’s disease and the music was used for a dance class intervention. The results showed no reports from the therapy literature documenting methods analysing recorded music used in therapy. The only methods reported for analysing recorded music were found in literature from the acoustic engineering field. These are critically evaluated with respect to using them in combination with existing music therapy methods.

Keywords: Music analysis, exercise, Parkinson's disease


This manuscript examines the rationale and validity of using various music analysis methods and instruments to assess the music used in a therapeutic intervention for people living with Parkinson’s disease. Parkinson’s disease is a progressive and debilitating neurological condition and currently has no cure (Shanahan, Morris, Ni Bhriain, Saunders, & Clifford, 2014). Dancing to music is one form of therapy that aims to increase physical activity and quality of life in people with this debilitating condition (Volpe, Signorini, Marchetto, Lynch, & Morris, 2013).

Gait issues and falls are common in people who have Parkinson’s disease (Morris et al., 2015). There is growing evidence that people with Parkinson’s disease can use the rhythmic elements in music to cue and sustain their movements (Shanahan, Morris, Bhriain, Volpe, Richardson, & Clifford, 2015; Thaut, 2013; Volpe et al. 2013). Concurrently, dance interventions are increasingly using rhythmical music to optimise movement, gait and postural control (Earhart, Ellis, Nieuwboer, & Dibble, 2012). There have been several studies examining the effects of mixed dance classes on movement (Heiberger et al., 2011), such as Irish set dancing (Volpe et al., 2013), and Argentinian tango dance forms for people with Parkinson’s disease (Shanahan et al., 2014; Volpe et al 2012). The contribution of music to dance performance in people with Parkinson’s disease has not been investigated. To date, the effects of the dance interventions have been measured and examined, but an examination of the music used in those interventions has been minimal or non-existent.

Music has the potential to improve exercise performance in unimpaired people and in those with movement disorders (Hamburg & Clair, 2003). It has been proposed that music can serve as a motivating agent (Karageorghis, Terry, & Lane, 1999) and in some instances music may reduce perceptions of fatigue, allowing the individual to exercise for longer periods (Karageorghis, Priest, Terry, Chatzisarantis, & Lane, 2006; Karageorghis, Terry, & Lane, 1999). Further, music can enable the individual to use the rhythm to entrain their movements to music which can also support them to exercise or perform physical activities for longer periods (Clair, Lyons, & Hamburg, 2012).

There are a number of factors that influence an individual’s response to music. These include, but are not limited to: age, sex, cognitive function, severity of stress, anxiety, discomfort and pain (Hamburg & Clair, 2003). The amount of formal training in music can affect how an individual processes musical information, as does their familiarity with and preference for the music (Pelletier, 2004). Additionally, the culture and prior personal associations an individual has with the music can affect their experience of the music during the therapy session (Hamburg & Clair, 2003; O'Callaghan, Dun, Baron, & Barry, 2013; Pelletier, 2004).

Analysis of music in the general community has mainly concentrated on the written musical form (sheet music). The focus is on the composer and their musical intentions, that is, what they wanted to be played, which instrumentation was used and how the music was interpreted (The Concise Oxford Dictionary of Music, 2007). Until recently it was not possible to objectively study the heard or aural version of music, especially in relation to music therapy.

A growing area of study is to examine music as it sounds, or as an acoustical event, one that is studied similarly to other sounds. The focus is on examining the physics of sound, the frequency, roll off, distortions, fluctuations and decibel levels (Fevotte, Bertin, & Durrieu, 2009). Putting this into a musical context, the music that is played and heard is studied, with all the possible variations in arrangement, instrumentation, musicians and interpretation (Knox, Cassidy, Beveridge, & MacDonald, 2008).

The tools for analysing recorded music typically examine the music as a sonorous or acoustic event (Knox et al., 2008) and the analysis is typically undertaken by acoustic engineers. Music is a sound to be analysed and treated with equal weight as any other sound. The “wav” format of the digital recording can be analysed by specialist software and objective acoustic information is distilled (Eerola & Toiviainen, 2004). The information can then be expressed in various visual formats other than musical notation. It is then possible to compare different pieces of music and find commonalities and differences.

Music therapists have utilised contrasting approaches to analysing the music used in music therapy interventions, typically to evaluate therapy outcomes and to prepare for the next session (Dillard, 2006; Gardstrom, 2003; Gilboa, 2012). These approaches examined the experience and response of individuals to music and music making, usually during the therapy sessions (Bruscia, 2002). This therapy based experience and an understanding of the relationship with the music can be the catalyst that brings about change in the client. These approaches also typically take into account individual responses to, and feelings for that music (Cepeda, Carr, Lau, & Alvarez, 2006).

One form of analysis involves focusing on the effects of song lyrics, both heard and written, during therapy sessions (Baker, Wigram, Stott, & McFerran, 2008; O'Callaghan & Grocke, 2009). The analysis is integral to the therapy session to enable therapists and clients to develop and gain personal insights (Gardstrom, 2003; McFerran, Baker, Patton, & Sawyer, 2006; O'Callaghan & Grocke, 2009). Non-vocal music that is improvised during a session can be analysed by a number of methods including:

The methods listed above rarely analyse the melodic and harmonic structure of the music that is used.

The music used in therapy or exercise interventions for people with movement disorders such as Parkinson’s disease has received minimal analysis. The focus has mainly been on the effects of the music, most commonly the effect of rhythm on movement (Hackney & Earhart, 2007). To date, there has been no comprehensive analysis of the music used in exercises or dancing classes for people with Parkinson’s that examine elements such as the melodic phrase length, tonality, instrumentation, tempi and rhythms.

The primary aim of this investigation was to identify valid tools and methods for evaluating the recorded music used in therapeutic dancing for people living with Parkinson’s disease. We also examined if the tools were used to quantify performance pre or post intervention; whether they were self-report tools; and if they were used in non-therapy applications such as sports, entertainment or in computer gaming.


A systematic search and critical evaluation of the following health and social sciences electronic databases from their earliest date to August 2014 was conducted:

See Appendix one for the MEDLINE search strategy.

A hand search of the following journals from the first available date was also conducted:

The health and social science databases do not include acoustic engineering journals. To find relevant articles about the analysis of music from an acoustic engineering perspective, a systematic search was conducted of the following databases:

Appendix two shows the engineering databases search strategy.

Selection Criteria

Articles from the Medline search were included if they were peer-reviewed studies for which: 1) the music analysis method was for participants who were receiving treatment, physical rehabilitation or dancing as part of a prescribed and structured therapy program with a clear goal of optimising, restoring or maintaining good health; and 2) the analysis tool was for music that was pre-recorded.

Articles from the engineering perspective were selected if they were peer-reviewed articles where: 1) the music analysis was for recorded music only; and 2) the music analysis was of the actual music, not of song lyrics.

Articles were excluded if: 1) the analysis method was for sessions with live music including improvised music; and 2) the participant was involved in the music making. These articles were excluded because we were examining the use of recorded music as a support for dance exercises only. For both search strategies articles in English, French, German and Russian were used. Translations for other languages were necessary.

Quality Assessment

A quality assessment of the health literature was conducted using the Downs & Black checklist (Downs & Black, 1998). The checklist was devised to measure methodological quality in non-randomised trials. There were 27 closed end questions that address: (1) overall study quality – 10 items; (2) external validity – three items; (3) study bias – seven items; (4) confounding and selection bias – six items; and (5) the power of the study – 1 item. Scores range from 0 to 28, with the following categories: excellent (26–28), good (20–25), fair (15–19) and poor (less than 14). This tool was not applicable for the engineering literature. These articles all came from peer–reviewed publications or conference proceedings.

Data Extraction

A checklist and data extraction form was devised by the authors to determine what the analysis method or approach the article contained, and, if it was useful to this project. There were five sections:

Data extraction was checked by a co-author. Copies of the Downs and Black checklist and the data extraction form are in tables one and two.

Figure 1. Flow chart of the studies included in the review.
Figure 1. Flow chart of the studies included in the review.

Table 1. Downs & Black quality analysis.
Study Country Study design Q1: Aim clearly described Q2: Outcomes clearly described Q3: Patients characteristics clearly described Q4: Interventions clearly described Q5: Principal confounders clearly described Q6: Main findings clearly described Q7: Random variability for the main outcome provided Q8: Adverse events reported
Beveridge & Knox 2009 UK Data gathering Y Y Y NA NA Y NA NAD
Bernardi 2006 Italy No control, AB design Y Y Y Y Y Y Y NAD
Forinash & Gonzalez 1989 USA Case study Y Y Y Y NA Y N NAD
Knox, Beveridge, Mitchell & MacDonaldet 2011 UK Repeated measures design Y Y Y Y Y Y N NAD
  Reporting   External validity Bias
Study Q9: Lost to follow up reported Q10: Actual p-value reported Q11: Sample asked to participate representative of the population Q12: Sample agreed to participate representative of the population Q13: Staff participating representative of the patient's environment Q14: Attempt to blind participants Q15: Attempt to blind assessors Q16: Data dredging results stated clearly Q17: Analysis adjusted for length of follow up Q18: Appropriate statistics
Beveridge & Knox 2009 NA NA Y Y N NA NA NA NA NA
Bernardi 2006 NA Y Y Y N N N NA NA Y
Forinash & Gonzalez 1989 NA NA Y Y Y NA NA NA NA NA
Knox & Beveridge et al 2011 NA NA Y Y NA N N NA NA Y
  Internal validity Selection bias Power  
Study Q19: Reliable compliance Q20: Accurate outcome measures Q21: Same population Q22: Participants recruited at the same time Q23: Randomised? Q24: Adequate allocation concealment? Q25: Adequate adjustment for confounders? Q26: Loss of follow up reported? Q27: Power calculation Final scores
Beveridge & Knox 2009 Unsure New measure N N N NA NA NA NA 6
Bernardi 2006 Y Y N Y N NA NA NA NA 14
Forinash & Gonzalez 1989 NA NA N NA NA NA NA NA NA 8
Knox & Beveridge et al 2011 NA Y N Y N NA NA NA NA 9
Key: Y = Yes, N=No, NA = Not Applicable, NAD = Nothing Adverse Detected
Table 2. Data extraction table
Author Title Journal or publication type Analysis Participant evaluation Researcher evaluation Pre or post intervention Used outside of therapy? Key findings
Bernardi et al 2006 Cardiovascular, cerebrovascular, and respiratory changes induced by different types of music in musicians and non-musicians: the importance of silence Heart Journal Breathing rate, ventilation, carbon dioxide, RR interval, blood pressure, mid- cerebral artery flow velocity, and baroreflex no yes both yes Compares the effect of rhythmic entrainment (RAS) on the cardiac system
Beveridge & Knox 2009 An exploration of the Effect of Structural and Acoustical Features on Perceived Musical Emotion Conference MaTrace, a MATLAB inspired propriety software yes yes post and during yes Examines the effect of music on emotions
Eerola & Toiviainen 2004 MIR in MATLAB: the MIDI toolbox Conference Electronic conversion tool no yes neither yes Tool for converting midi music recordings into data that can be read by non-musicians. May not work for all recordings
Forinash & Gonzalez 1989 A phenomenological perspective of music therapy. 7 step qual process for documenting each MT session Journal of Music Therapy A qualitative process for documenting each MT session, after the session no yes post no Good for 1:1 sessions and psychodynamic sessions
Knox, Beveridge, Mitchell & MacDonaldet 2011 Acoustic analysis and mood classification of pain-relieving music Journal of Acoustic Society America Compares the musical features of the differing music chosen by subjects to relieve pain yes yes post yes Music expressed contentment after acoustical analysis, despite being different in content, genre and tonality



Hand searches of the selected journals did not reveal any additional relevant papers that had not already been identified in the electronic search. The combined allied health and engineering electronic searches initially yielded 40 references. Non-peer reviewed articles and duplicates were removed, resulting in 30 references. Review of the titles and abstracts excluded another 14 references leaving 16. Citation tracking and consultation with the co-author resulted in five usable articles: Four from the health sciences and one from an engineering perspective (Bernardi et al., 2009; Eerola & Toiviainen, 2004; Forinash & Gonzalez, 1989; Knox, Beveridge, Mitchell, & MacDonald, 2011).

Results of Quality Analysis

A quality analysis was conducted of the four health science journals using the Downs and Black tool. The overall scores were low, ranging from 6 to 14 (out of a possible score of 28) reflecting the experiential nature and size of the studies. For each study, blinding of assessor or participant, randomisation of participants and concealment were not possible. In this emerging research field, none of the researchers conducted formal statistical analyses of their findings, and in the case of Beveridge and Knox (2009), a new outcome measure was used without prior validation of its psychometric properties.


The search outcomes showed that a single tool for analysing the music used for therapy does not currently exist. In addition there were no tools for analysing recorded music used in Parkinson’s disease exercise classes or dancing classes. This was of particular importance as the music used in most therapeutic dance classes is recorded, and is chosen by the dance teacher. Usually there are no live musicians and the participants are not involved in music making.

All of the existing music therapy analysis methods examined client experiences and their interpretation of the music (McFerran & Wigram, 2005; O'Callaghan & Grocke, 2009). The music was sometimes played live and often involved both the client and therapist writing lyrics, composing music to suit, or improvising music during the session. This type of therapeutic music making and song writing has psychodynamic aims such as increasing self-confidence, developing insight and clarifying emotions (Baker et al., 2008). The emphasis is typically on the phenomenological component, that is, the client experience “in the moment” of therapy (Gilboa, 2012). To comprehensively analyse music for therapy interventions, an understanding of the musical features of the music is helpful. This could include the melody, harmony, rhythm, tempo and lyrics, and how they affect the client.

The search outcomes also showed that few guidelines exist for the selection and evaluation of music for exercise classes or movement rehabilitation, particularly for those with movement disorders such as Parkinson’s disease. Karageorghis (Karageorghis, Terry, & Lane, 1999) developed the Brunel Music Rating Inventory, which was refined in 2006 and adopted for use in the British Association of Sport and Exercise Sciences Expert (BASES) statement on the use of music in exercise. (Karageorghis, Priest, Terry, Chatzisarantis & Lane, 2006) The BASES guidelines are broad with regards to choosing what music to use. They suggest that the music chosen should reflect the musical tastes of the client and it should incorporate their suggestions, have a strong rhythm that matches the activity and have a tempo within 125 – 150 beats per minute. The Brunel inventory has two sections. The first prompts the individual to imagine what music they would consider to be motivating. The second is used after the client has heard a piece of music. Subsequent to undertaking the exercises, the inventory is used to analyse the music and inform decision making for future sessions. The BASES statement and the Brunel Inventory were both originally developed in the context of the needs of elite athletes to improve competition performance (Karageorghis & Priest, 2012). To date they have not been applied to clinical populations such as Parkinson’s disease.

Contemporary analysis made by acoustic engineers of the moods evoked by music is done on single, brief events of music such as a chord or a phrase. It does not take into account how expectations can be set up by music, either fulfilling or surprising the listener (Knox et al., 2011). This method also does not take into account that music associations are very subjective. The same music can be relaxing for some people and stimulating to others (Hamburg & Clair, 2003).

The most recent development is a combination of computerised “MATLAB” software and mood analysis (Eerola & Toiviainen, 2004). It looks at music used to motivate the listener. Personal or individual connections to the music are included in such analyses (Eerola & Vuoskoski, 2013). The future development of this software will establish any commonalities between music that has been chosen by many different people, for a common purpose. That is, comparing the music self-selected as a good distractor during similar procedures, between different participants. Commonalities may include the length of the music, tonality of music, melodic phrase length, harmonic features and presence of lyrics (Knox et al, 2011).

There are two important considerations to keep in mind when interpreting the results of this review. First, the existing music therapy knowledge base does not include an awareness of complex acoustic analysis software, and, those with knowledge of the software are not therapists, and may not have formal music training. Second, previous studies did not usually note if the music was familiar to the participants, liked by them, or chosen by them. It was not possible to ascertain whether therapeutic outcomes varied for known music compared to unfamiliar music.


There is currently no single tool for analysing the music used for therapy. Existing therapy analysis tools are typically qualitative and examine the interactions and relationships between the client and live music, in the moment of therapy. These provide some indication that the music used in therapeutic dance interventions can contribute to the success of the class. In many cases it transformed the experience for the participants and supported them in optimising their movements. On the other hand, music analysis software objectively examines the acoustic profile of recorded music. Combining these methods would result in a more thorough examination and subsequent understanding of recorded music used in therapy sessions.


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Bergstrøm-Nielsen, C. (1993). Graphic Notation as a Tool in Describing and Analyzing Music Therapy Improvisations. Music Therapy, 12(1), 40-58. doi: 10.1093/mt/12.1.40

Bernardi, L., Porta, C., & Sleight, P. (2006). Cardiovasular, cerebrovascular and respiratory changes induced by different types of music in musicians and non-musicians: the importance of silence. Heart, 92(4), 445-452

Bernardi, L., Porta, C., Casucci, G., Balsamo, R., Bernardi, N. F., Fogari, R., & Sleight, P. (2009). Dynamic interactions between musical, cardiovascular, and cerebral rhythms in humans. Circulation, 119(25), 3171-3180. doi: 10.1161/CIRCULATIONAHA.108.806174

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Appendix one - Medline search, music therapy analysis tools for music as used in therapy sessions.

Step Search terms Results
1 (music analysis or "analysis of music").mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier] 41
2 analysis of [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier] 28
3 music [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier] 14
4 2 or 3 41
5 therap*.mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept, rare disease supplementary concept, unique identifier] 2490443
6 4 and 5 16



Appendix two – engineering databases search, a system for the analysis of musical data, acoustic engineering perspective.

Set Searched for Databases Results
1 all("property maintenance") ASFA: Aquatic Sciences and Fisheries Abstracts, British Periodicals, Design and Applied Arts Index (DAAI), ERIC, GeoRef, Index Islamicus, Linguistics and Language Behavior Abstracts (LLBA), PAIS International, Periodicals Archive Online, PILOTS: Published International Literature On Traumatic Stress, ProQuest Australia & New Zealand Newsstand, ProQuest Central, ProQuest Dissertations & Theses: UK & Ireland, ProQuest Dissertations & Theses Full Text, ProQuest Historical Newspapers: Los Angeles Times (1881-1989), ProQuest Historical Newspapers: The Guardian (1821-2003) and The Observer (1791-2003), ProQuest Historical Newspapers: The New York Times (1851-2009), ProQuest Historical Newspapers: The Times of India (1838-2003), ProQuest Historical Newspapers: The Washington Post (1877-1996), Social Services Abstracts, Sociological Abstracts, Water Resources Abstracts 5631
2 all(property) AND all((maintain* OR maintenance)) AND all(essential OR importan* or value OR necessary) As above 45409
3 all(property and maintenance) AND all(essential OR importan* OR value OR necessary) As above 13379
4 ti(property AND maintenance) AND all(essential OR importan* OR value OR necessary) As above 147
5 ti("property maintenance") AND all(reason*) As above 3
6 ti("property maintenance") As above 1640
7 ti("property maintenance") AND all(hotel*) As above 0
8 all("property maintenance") AND all(hotel*) As above 7
9 all("music analysis" or "analyzing music*") AND all((prerecorded OR "pre-recorded" OR recorded)) As above 30


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